Distances (v1.0.1)

Fast and generic distance functions for high-dimensional data.

Usage

Add this to your project:

```shell

cargo add distances@1.0.1 ```

Use it in your project:

```rust use distances::Number; use distances::vectors::euclidean;

let a = [1.0f32, 2.0, 3.0]; let b = [4.0f32, 5.0, 6.0];

let distance: f32 = euclidean(&a, &b);

assert!((distance - (27.0_f32).sqrt()).abs() < 1e-6); ```

Features

• [x] A Number trait to abstract over different numeric types.
  • [x] Distance functions are generic over the return type implementing Number.
  • [x] Distance functions may also be generic over the input type being a collection of Numbers.
• [ ] SIMD accelerated implementations for float types.
• [ ] Python bindings with maturin and pyo3.
• [ ] no_std support.

Available Distance Functions

• [ ] Vectors (high-dimensional data):
  • [x] euclidean
  • [x] squared_euclidean
  • [x] manhattan
  • [x] chebyshev
  • [ ] minkowski
  • [ ] minkowski_p
  • [x] cosine
  • [x] hamming
  • [x] canberra
  • Canberra Distance
  • [ ] bray_curtis
  • Bray-Curtis Distance
  • [ ] pearson
  • 1.0 - r where r is the Pearson Correlation Coefficient
• [ ] Probability distributions:
  • [ ] wasserstein
  • Wasserstein Distance
  • [ ] bhattacharyya
  • Bhattacharyya Distance
  • [ ] hellinger
  • Hellinger Distance
• [ ] String data, e.g. for genomic sequences:
  • [x] levenshtein
  • [ ] needleman_wunsch
  • [ ] smith_waterman
  • [x] hamming
  • [ ] Normalized versions of the above.
• [ ] Sets:
  • [x] jaccard
  • [ ] hausdorff
  • Hausdorff Distance
• [ ] Graphs:
  • [ ] tanamoto
• [ ] Time series:
  • [ ] dtw
  • Dynamic Time Warping
  • [ ] msm
  • Move-Split-Merge
  • [ ] erp
  • Edit distance with Real Penalty

Contributing

Contributions are welcome, encouraged, and appreciated! See CONTRIBUTING.md.

License

Licensed under the MIT license.